Overview

Dataset statistics

Number of variables23
Number of observations52
Missing cells557
Missing cells (%)46.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory186.5 B

Variable types

Numeric14
Unsupported6
Categorical3

Alerts

Coal has constant value "4.99"Constant
Firewood has constant value "13.24"Constant
Oil has 52 (100.0%) missing valuesMissing
Coal has 51 (98.1%) missing valuesMissing
Hydroenergy has 52 (100.0%) missing valuesMissing
Nuclear has 52 (100.0%) missing valuesMissing
Firewood has 51 (98.1%) missing valuesMissing
Sugarcane and products has 1 (1.9%) missing valuesMissing
Other Primary_x000d_ has 52 (100.0%) missing valuesMissing
LPG has 15 (28.8%) missing valuesMissing
Gasoline/alcohol has 41 (78.8%) missing valuesMissing
Kerosene/jet fuel has 36 (69.2%) missing valuesMissing
Coke has 49 (94.2%) missing valuesMissing
Charcoal has 52 (100.0%) missing valuesMissing
Gases has 1 (1.9%) missing valuesMissing
Non-energy has 52 (100.0%) missing valuesMissing
Year is uniformly distributedUniform
Coke is uniformly distributedUniform
Year has unique valuesUnique
Natural gas has unique valuesUnique
Total Primaries has unique valuesUnique
Electricity has unique valuesUnique
Diesel oil has unique valuesUnique
Fuel oil has unique valuesUnique
Other secondary has unique valuesUnique
Total Secundaries has unique valuesUnique
Total has unique valuesUnique
Oil is an unsupported type, check if it needs cleaning or further analysisUnsupported
Hydroenergy is an unsupported type, check if it needs cleaning or further analysisUnsupported
Nuclear is an unsupported type, check if it needs cleaning or further analysisUnsupported
Other Primary_x000d_ is an unsupported type, check if it needs cleaning or further analysisUnsupported
Charcoal is an unsupported type, check if it needs cleaning or further analysisUnsupported
Non-energy is an unsupported type, check if it needs cleaning or further analysisUnsupported
Gasoline/alcohol has 1 (1.9%) zerosZeros

Reproduction

Analysis started2023-07-30 07:42:40.258958
Analysis finished2023-07-30 07:43:30.001713
Duration49.74 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.5
Minimum1970
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:30.167360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972.55
Q11982.75
median1995.5
Q32008.25
95-th percentile2018.45
Maximum2021
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075944662
Kurtosis-1.2
Mean1995.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103766
Variance229.66667
MonotonicityStrictly increasing
2023-07-30T07:43:30.429740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 1
 
1.9%
1971 1
 
1.9%
1998 1
 
1.9%
1999 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1970 1
1.9%
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
ValueCountFrequency (%)
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%
2012 1
1.9%

Oil
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Natural gas
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2281.7448
Minimum68.43
Maximum7216.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:30.684507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum68.43
5-th percentile92.4585
Q1433.3625
median1032.71
Q34063.8525
95-th percentile6533.8515
Maximum7216.49
Range7148.06
Interquartile range (IQR)3630.49

Descriptive statistics

Standard deviation2273.1402
Coefficient of variation (CV)0.99622892
Kurtosis-0.81692365
Mean2281.7448
Median Absolute Deviation (MAD)922.15
Skewness0.80668065
Sum118650.73
Variance5167166.2
MonotonicityNot monotonic
2023-07-30T07:43:30.939366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.43 1
 
1.9%
86.77 1
 
1.9%
1290.93 1
 
1.9%
1485.92 1
 
1.9%
1993.92 1
 
1.9%
2121.93 1
 
1.9%
2538.79 1
 
1.9%
2732.18 1
 
1.9%
2940.94 1
 
1.9%
3090.72 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
68.43 1
1.9%
86.77 1
1.9%
91.43 1
1.9%
93.3 1
1.9%
127.82 1
1.9%
136.21 1
1.9%
139.01 1
1.9%
145.54 1
1.9%
149.27 1
1.9%
150.21 1
1.9%
ValueCountFrequency (%)
7216.49 1
1.9%
6580.29 1
1.9%
6543.13 1
1.9%
6526.26 1
1.9%
6290.71 1
1.9%
6096.36 1
1.9%
5808.27 1
1.9%
5243.68 1
1.9%
4981.66 1
1.9%
4973.66 1
1.9%

Coal
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing51
Missing (%)98.1%
Memory size548.0 B
4.99

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row4.99

Common Values

ValueCountFrequency (%)
4.99 1
 
1.9%
(Missing) 51
98.1%

Length

2023-07-30T07:43:31.184668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:43:31.382924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.99 1
100.0%

Most occurring characters

ValueCountFrequency (%)
9 2
50.0%
4 1
25.0%
. 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3
75.0%
Other Punctuation 1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2
66.7%
4 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2
50.0%
4 1
25.0%
. 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2
50.0%
4 1
25.0%
. 1
25.0%

Hydroenergy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Nuclear
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Firewood
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing51
Missing (%)98.1%
Memory size548.0 B
13.24

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row13.24

Common Values

ValueCountFrequency (%)
13.24 1
 
1.9%
(Missing) 51
98.1%

Length

2023-07-30T07:43:31.544092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:43:31.746796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
13.24 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
20.0%
3 1
20.0%
. 1
20.0%
2 1
20.0%
4 1
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
80.0%
Other Punctuation 1
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
25.0%
3 1
25.0%
2 1
25.0%
4 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
20.0%
3 1
20.0%
. 1
20.0%
2 1
20.0%
4 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
20.0%
3 1
20.0%
. 1
20.0%
2 1
20.0%
4 1
20.0%

Sugarcane and products
Real number (ℝ)

Distinct51
Distinct (%)100.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean6955.1431
Minimum73.66
Maximum15153.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:31.945630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum73.66
5-th percentile102.4
Q13773.935
median7109.32
Q310469.575
95-th percentile13684.49
Maximum15153.56
Range15079.9
Interquartile range (IQR)6695.64

Descriptive statistics

Standard deviation4429.1032
Coefficient of variation (CV)0.63680978
Kurtosis-0.8907345
Mean6955.1431
Median Absolute Deviation (MAD)3408.48
Skewness-0.04092043
Sum354712.3
Variance19616955
MonotonicityNot monotonic
2023-07-30T07:43:32.205495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.2 1
 
1.9%
84.09 1
 
1.9%
7477.83 1
 
1.9%
6751.67 1
 
1.9%
5522.68 1
 
1.9%
5834.35 1
 
1.9%
6393.39 1
 
1.9%
7371.17 1
 
1.9%
7460.8 1
 
1.9%
8081.19 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
73.66 1
1.9%
84.09 1
1.9%
89.2 1
1.9%
115.6 1
1.9%
125.18 1
1.9%
129.65 1
1.9%
138.59 1
1.9%
563.93 1
1.9%
1235.8 1
1.9%
2012.84 1
1.9%
ValueCountFrequency (%)
15153.56 1
1.9%
14309.58 1
1.9%
14051.31 1
1.9%
13317.67 1
1.9%
13167.47 1
1.9%
12887.1 1
1.9%
12789.01 1
1.9%
12477.99 1
1.9%
12269.63 1
1.9%
12248.44 1
1.9%

Other Primary_x000d_
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Total Primaries
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9103.4846
Minimum157.63
Maximum21733.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:32.460651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum157.63
5-th percentile198.4765
Q14548.0675
median8122.505
Q314582.733
95-th percentile19132.451
Maximum21733.85
Range21576.22
Interquartile range (IQR)10034.665

Descriptive statistics

Standard deviation6438.7431
Coefficient of variation (CV)0.70728335
Kurtosis-0.88515031
Mean9103.4846
Median Absolute Deviation (MAD)5176.71
Skewness0.33297421
Sum473381.2
Variance41457413
MonotonicityNot monotonic
2023-07-30T07:43:32.717355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.63 1
 
1.9%
170.85 1
 
1.9%
8768.76 1
 
1.9%
8237.59 1
 
1.9%
7516.61 1
 
1.9%
7956.28 1
 
1.9%
8932.18 1
 
1.9%
10103.35 1
 
1.9%
10401.74 1
 
1.9%
11171.91 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
157.63 1
1.9%
166.95 1
1.9%
170.85 1
1.9%
221.08 1
1.9%
251.81 1
1.9%
252.99 1
1.9%
277.6 1
1.9%
713.21 1
1.9%
1381.34 1
1.9%
2185.39 1
1.9%
ValueCountFrequency (%)
21733.85 1
1.9%
21526.06 1
1.9%
19263.83 1
1.9%
19024.96 1
1.9%
18791.57 1
1.9%
18768.7 1
1.9%
18463.27 1
1.9%
18231.82 1
1.9%
17593.18 1
1.9%
17251.29 1
1.9%

Electricity
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1123.0137
Minimum178.81
Maximum3333.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:32.969856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum178.81
5-th percentile211.5525
Q1430.2975
median744.505
Q31562.2325
95-th percentile2774.214
Maximum3333.63
Range3154.82
Interquartile range (IQR)1131.935

Descriptive statistics

Standard deviation925.57501
Coefficient of variation (CV)0.82418856
Kurtosis-0.24351271
Mean1123.0137
Median Absolute Deviation (MAD)422.345
Skewness1.0338974
Sum58396.71
Variance856689.09
MonotonicityNot monotonic
2023-07-30T07:43:33.220980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.81 1
 
1.9%
210.7 1
 
1.9%
825.74 1
 
1.9%
896.11 1
 
1.9%
900.83 1
 
1.9%
958.86 1
 
1.9%
997.87 1
 
1.9%
1029.94 1
 
1.9%
1099.61 1
 
1.9%
1186.1 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
178.81 1
1.9%
209.5 1
1.9%
210.7 1
1.9%
212.25 1
1.9%
220.24 1
1.9%
224.03 1
1.9%
264.52 1
1.9%
274.4 1
1.9%
305.44 1
1.9%
338.88 1
1.9%
ValueCountFrequency (%)
3333.63 1
1.9%
3287.51 1
1.9%
2810.69 1
1.9%
2744.37 1
1.9%
2699.1 1
1.9%
2680.88 1
1.9%
2554.05 1
1.9%
2552.28 1
1.9%
2542.88 1
1.9%
2302.27 1
1.9%

LPG
Real number (ℝ)

Distinct36
Distinct (%)97.3%
Missing15
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean21.95
Minimum0.29
Maximum78.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:33.467130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile3.696
Q113.02
median17.44
Q326.06
95-th percentile55.978
Maximum78.04
Range77.75
Interquartile range (IQR)13.04

Descriptive statistics

Standard deviation17.038965
Coefficient of variation (CV)0.77626263
Kurtosis2.5297107
Mean21.95
Median Absolute Deviation (MAD)6.56
Skewness1.5771204
Sum812.15
Variance290.32632
MonotonicityNot monotonic
2023-07-30T07:43:33.710765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
9.6 2
 
3.8%
18.96 1
 
1.9%
10.53 1
 
1.9%
41.48 1
 
1.9%
45.72 1
 
1.9%
27.37 1
 
1.9%
57.05 1
 
1.9%
53.01 1
 
1.9%
18.28 1
 
1.9%
55.71 1
 
1.9%
Other values (26) 26
50.0%
(Missing) 15
28.8%
ValueCountFrequency (%)
0.29 1
1.9%
0.64 1
1.9%
4.46 1
1.9%
5.26 1
1.9%
5.57 1
1.9%
9.6 2
3.8%
10.53 1
1.9%
12.38 1
1.9%
13.02 1
1.9%
13.57 1
1.9%
ValueCountFrequency (%)
78.04 1
1.9%
57.05 1
1.9%
55.71 1
1.9%
53.01 1
1.9%
45.72 1
1.9%
41.48 1
1.9%
29.32 1
1.9%
29.05 1
1.9%
27.37 1
1.9%
26.06 1
1.9%

Gasoline/alcohol
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)72.7%
Missing41
Missing (%)78.8%
Infinite0
Infinite (%)0.0%
Mean7.0190909
Minimum0
Maximum29.45
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:33.930285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.895
Q13.79
median3.83
Q37.175
95-th percentile20.01
Maximum29.45
Range29.45
Interquartile range (IQR)3.385

Descriptive statistics

Standard deviation8.0061407
Coefficient of variation (CV)1.1406236
Kurtosis7.2780111
Mean7.0190909
Median Absolute Deviation (MAD)0.04
Skewness2.570435
Sum77.21
Variance64.098289
MonotonicityNot monotonic
2023-07-30T07:43:34.115843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.79 3
 
5.8%
3.83 2
 
3.8%
9.82 1
 
1.9%
4.53 1
 
1.9%
29.45 1
 
1.9%
10.57 1
 
1.9%
3.81 1
 
1.9%
0 1
 
1.9%
(Missing) 41
78.8%
ValueCountFrequency (%)
0 1
 
1.9%
3.79 3
5.8%
3.81 1
 
1.9%
3.83 2
3.8%
4.53 1
 
1.9%
9.82 1
 
1.9%
10.57 1
 
1.9%
29.45 1
 
1.9%
ValueCountFrequency (%)
29.45 1
 
1.9%
10.57 1
 
1.9%
9.82 1
 
1.9%
4.53 1
 
1.9%
3.83 2
3.8%
3.81 1
 
1.9%
3.79 3
5.8%
0 1
 
1.9%

Kerosene/jet fuel
Real number (ℝ)

Distinct13
Distinct (%)81.2%
Missing36
Missing (%)69.2%
Infinite0
Infinite (%)0.0%
Mean4.229375
Minimum0.82
Maximum16.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:34.317762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.82
5-th percentile0.8275
Q10.85
median0.865
Q34.5575
95-th percentile15.2275
Maximum16.63
Range15.81
Interquartile range (IQR)3.7075

Descriptive statistics

Standard deviation5.4711443
Coefficient of variation (CV)1.2936059
Kurtosis1.0837623
Mean4.229375
Median Absolute Deviation (MAD)0.04
Skewness1.5829264
Sum67.67
Variance29.93342
MonotonicityNot monotonic
2023-07-30T07:43:34.527782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.86 3
 
5.8%
0.85 2
 
3.8%
16.63 1
 
1.9%
0.82 1
 
1.9%
0.83 1
 
1.9%
3.47 1
 
1.9%
4.34 1
 
1.9%
14.76 1
 
1.9%
13.02 1
 
1.9%
5.21 1
 
1.9%
Other values (3) 3
 
5.8%
(Missing) 36
69.2%
ValueCountFrequency (%)
0.82 1
 
1.9%
0.83 1
 
1.9%
0.84 1
 
1.9%
0.85 2
3.8%
0.86 3
5.8%
0.87 1
 
1.9%
2.6 1
 
1.9%
3.47 1
 
1.9%
4.34 1
 
1.9%
5.21 1
 
1.9%
ValueCountFrequency (%)
16.63 1
 
1.9%
14.76 1
 
1.9%
13.02 1
 
1.9%
5.21 1
 
1.9%
4.34 1
 
1.9%
3.47 1
 
1.9%
2.6 1
 
1.9%
0.87 1
 
1.9%
0.86 3
5.8%
0.85 2
3.8%

Diesel oil
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400.28654
Minimum53.15
Maximum1510.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:34.938825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum53.15
5-th percentile58.233
Q1151.6225
median256.825
Q3472.445
95-th percentile1240.072
Maximum1510.1
Range1456.95
Interquartile range (IQR)320.8225

Descriptive statistics

Standard deviation367.46679
Coefficient of variation (CV)0.91800935
Kurtosis1.4951895
Mean400.28654
Median Absolute Deviation (MAD)124.265
Skewness1.524129
Sum20814.9
Variance135031.84
MonotonicityNot monotonic
2023-07-30T07:43:35.391271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.73 1
 
1.9%
53.15 1
 
1.9%
210.7 1
 
1.9%
247.32 1
 
1.9%
260.53 1
 
1.9%
265.88 1
 
1.9%
88.87 1
 
1.9%
153.19 1
 
1.9%
147.2 1
 
1.9%
57.1 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
53.15 1
1.9%
55.73 1
1.9%
57.1 1
1.9%
59.16 1
1.9%
88.87 1
1.9%
92.56 1
1.9%
113.17 1
1.9%
125.17 1
1.9%
131.32 1
1.9%
139.74 1
1.9%
ValueCountFrequency (%)
1510.1 1
1.9%
1335.81 1
1.9%
1304.73 1
1.9%
1187.17 1
1.9%
972.27 1
1.9%
923.68 1
1.9%
903.45 1
1.9%
887.01 1
1.9%
886.91 1
1.9%
839.66 1
1.9%

Fuel oil
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1104.7033
Minimum108.65
Maximum1797.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:35.846824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum108.65
5-th percentile173.1355
Q1921.385
median1130.17
Q31549.56
95-th percentile1749.0865
Maximum1797.83
Range1689.18
Interquartile range (IQR)628.175

Descriptive statistics

Standard deviation525.74612
Coefficient of variation (CV)0.47591614
Kurtosis-0.79711591
Mean1104.7033
Median Absolute Deviation (MAD)403.285
Skewness-0.63486397
Sum57444.57
Variance276408.98
MonotonicityNot monotonic
2023-07-30T07:43:36.291106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
885.49 1
 
1.9%
933.35 1
 
1.9%
1702.17 1
 
1.9%
1276.01 1
 
1.9%
1029.96 1
 
1.9%
990.62 1
 
1.9%
993.24 1
 
1.9%
1139.85 1
 
1.9%
1034.9 1
 
1.9%
1093.99 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
108.65 1
1.9%
131.89 1
1.9%
168.51 1
1.9%
176.92 1
1.9%
180.78 1
1.9%
202.03 1
1.9%
237.66 1
1.9%
303.96 1
1.9%
341.21 1
1.9%
345.73 1
1.9%
ValueCountFrequency (%)
1797.83 1
1.9%
1784.92 1
1.9%
1783.72 1
1.9%
1720.75 1
1.9%
1712.29 1
1.9%
1702.17 1
1.9%
1643.69 1
1.9%
1640.92 1
1.9%
1621.17 1
1.9%
1596.58 1
1.9%

Coke
Categorical

MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing49
Missing (%)94.2%
Memory size548.0 B
9.66
8.28
6.9

Length

Max length4
Median length4
Mean length3.6666667
Min length3

Characters and Unicode

Total characters11
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row9.66
2nd row8.28
3rd row6.9

Common Values

ValueCountFrequency (%)
9.66 1
 
1.9%
8.28 1
 
1.9%
6.9 1
 
1.9%
(Missing) 49
94.2%

Length

2023-07-30T07:43:36.731583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:43:37.067535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9.66 1
33.3%
8.28 1
33.3%
6.9 1
33.3%

Most occurring characters

ValueCountFrequency (%)
. 3
27.3%
6 3
27.3%
9 2
18.2%
8 2
18.2%
2 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
72.7%
Other Punctuation 3
 
27.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 3
37.5%
9 2
25.0%
8 2
25.0%
2 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3
27.3%
6 3
27.3%
9 2
18.2%
8 2
18.2%
2 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3
27.3%
6 3
27.3%
9 2
18.2%
8 2
18.2%
2 1
 
9.1%

Charcoal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Gases
Real number (ℝ)

Distinct51
Distinct (%)100.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean248.04216
Minimum85.97
Maximum407.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:37.438285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum85.97
5-th percentile94.775
Q1171.325
median206.32
Q3344.21
95-th percentile379.765
Maximum407.95
Range321.98
Interquartile range (IQR)172.885

Descriptive statistics

Standard deviation97.271818
Coefficient of variation (CV)0.39215841
Kurtosis-1.3993313
Mean248.04216
Median Absolute Deviation (MAD)94.54
Skewness-0.0044986451
Sum12650.15
Variance9461.8066
MonotonicityNot monotonic
2023-07-30T07:43:37.715484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.97 1
 
1.9%
88.11 1
 
1.9%
337.99 1
 
1.9%
287.86 1
 
1.9%
318.43 1
 
1.9%
327.96 1
 
1.9%
299.16 1
 
1.9%
314.2 1
 
1.9%
304.44 1
 
1.9%
300.86 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
85.97 1
1.9%
88.11 1
1.9%
90.26 1
1.9%
99.29 1
1.9%
103.16 1
1.9%
122.93 1
1.9%
140.57 1
1.9%
159.06 1
1.9%
162.92 1
1.9%
163.35 1
1.9%
ValueCountFrequency (%)
407.95 1
1.9%
393.72 1
1.9%
385.99 1
1.9%
373.54 1
1.9%
373.52 1
1.9%
364.1 1
1.9%
364.06 1
1.9%
361.08 1
1.9%
358.32 1
1.9%
356.76 1
1.9%

Other secondary
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2231.2613
Minimum181.49
Maximum3988.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:37.990546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum181.49
5-th percentile506.96
Q11302.4725
median2083.92
Q33241.735
95-th percentile3724.6485
Maximum3988.67
Range3807.18
Interquartile range (IQR)1939.2625

Descriptive statistics

Standard deviation1112.7398
Coefficient of variation (CV)0.49870436
Kurtosis-1.3813019
Mean2231.2613
Median Absolute Deviation (MAD)994.63
Skewness-0.040811521
Sum116025.59
Variance1238189.8
MonotonicityNot monotonic
2023-07-30T07:43:38.266444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181.49 1
 
1.9%
469.12 1
 
1.9%
2518.59 1
 
1.9%
2369.2 1
 
1.9%
2656.18 1
 
1.9%
2950.37 1
 
1.9%
3068.38 1
 
1.9%
3078.13 1
 
1.9%
3360.8 1
 
1.9%
3365.97 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
181.49 1
1.9%
431.12 1
1.9%
469.12 1
1.9%
537.92 1
1.9%
874.69 1
1.9%
887.14 1
1.9%
896.31 1
1.9%
908.76 1
1.9%
1138.08 1
1.9%
1140.05 1
1.9%
ValueCountFrequency (%)
3988.67 1
1.9%
3959.76 1
1.9%
3752.33 1
1.9%
3702 1
1.9%
3599.74 1
1.9%
3584.92 1
1.9%
3570.78 1
1.9%
3564.06 1
1.9%
3552.95 1
1.9%
3528.56 1
1.9%

Non-energy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Total Secundaries
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5121.4185
Minimum1397.13
Maximum8653.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:38.535203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1397.13
5-th percentile2123.2245
Q13747.155
median4925.51
Q36631.88
95-th percentile7797.601
Maximum8653.28
Range7256.15
Interquartile range (IQR)2884.725

Descriptive statistics

Standard deviation1811.9472
Coefficient of variation (CV)0.35379792
Kurtosis-0.72953573
Mean5121.4185
Median Absolute Deviation (MAD)1380.37
Skewness0.011024684
Sum266313.76
Variance3283152.7
MonotonicityNot monotonic
2023-07-30T07:43:38.789374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1397.13 1
 
1.9%
1762.72 1
 
1.9%
5619.15 1
 
1.9%
5100.84 1
 
1.9%
5226.28 1
 
1.9%
5502.79 1
 
1.9%
5462.69 1
 
1.9%
5756.8 1
 
1.9%
5992.67 1
 
1.9%
6031.38 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1397.13 1
1.9%
1762.72 1
1.9%
1895.97 1
1.9%
2309.16 1
1.9%
2733.08 1
1.9%
2882 1
1.9%
3120.06 1
1.9%
3198.11 1
1.9%
3499.82 1
1.9%
3519.57 1
1.9%
ValueCountFrequency (%)
8653.28 1
1.9%
8495.31 1
1.9%
8067.97 1
1.9%
7576.39 1
1.9%
7537.05 1
1.9%
7505.97 1
1.9%
7284.54 1
1.9%
7268.95 1
1.9%
7258.66 1
1.9%
7096.33 1
1.9%

Total
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14224.902
Minimum1554.76
Maximum29002.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:43:39.062139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1554.76
5-th percentile2319.946
Q18248.165
median12995.825
Q321294.272
95-th percentile27573.702
Maximum29002.79
Range27448.03
Interquartile range (IQR)13046.108

Descriptive statistics

Standard deviation8117.6472
Coefficient of variation (CV)0.57066453
Kurtosis-0.93654784
Mean14224.902
Median Absolute Deviation (MAD)6514.96
Skewness0.23731889
Sum739694.92
Variance65896196
MonotonicityNot monotonic
2023-07-30T07:43:39.313880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1554.76 1
 
1.9%
1933.58 1
 
1.9%
14387.91 1
 
1.9%
13338.44 1
 
1.9%
12742.89 1
 
1.9%
13459.06 1
 
1.9%
14394.87 1
 
1.9%
15860.15 1
 
1.9%
16394.41 1
 
1.9%
17203.29 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1554.76 1
1.9%
1933.58 1
1.9%
2062.92 1
1.9%
2530.24 1
1.9%
2986.07 1
1.9%
3159.6 1
1.9%
3371.86 1
1.9%
3911.32 1
1.9%
4881.16 1
1.9%
5883.42 1
1.9%
ValueCountFrequency (%)
29002.79 1
1.9%
28609.87 1
1.9%
27759.14 1
1.9%
27421.98 1
1.9%
26309.5 1
1.9%
26297.54 1
1.9%
26000.32 1
1.9%
24851.84 1
1.9%
24543.14 1
1.9%
24235.56 1
1.9%

Interactions

2023-07-30T07:43:25.669020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:40.732714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:46.386800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:49.389332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:52.429303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:55.851175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:59.485286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:02.394840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:05.131612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:08.042412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:12.171440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:15.199457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:18.279851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:21.595698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:26.066109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:41.278626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:46.794440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:49.813163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:52.827612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:56.486160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:59.828552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:02.568484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:05.348407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:08.683348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:12.561746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:15.606122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:18.683093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:22.147571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:26.257497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:41.749476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:46.984900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:50.007633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:53.022931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:56.826574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:00.030652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:02.764916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:05.558602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:08.981817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:12.770759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:15.805339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:18.897004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:22.428258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:26.466785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:42.275522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:47.209822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:50.224451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:53.218070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:57.159731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:00.203794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:02.968047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:05.756404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:09.280648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:12.966524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:16.002601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:19.125835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:22.780644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:26.639218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:42.695816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:47.422594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:50.417422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:53.377971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:57.478991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:00.420211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:03.165440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:05.969852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:09.590752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:13.155287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:16.205761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:19.320979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:23.094167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:26.826605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:43.201703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:47.605623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:50.615500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:53.564786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:57.745906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:00.601353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:03.343304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:06.177965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:09.911564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:13.342859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:16.395813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:19.509328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:23.419744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:27.031983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:43.587116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:47.808020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:50.789148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:53.773945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:57.949845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:00.823258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:03.509531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:06.358560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:10.256930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:13.573385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:16.601553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:19.738674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:23.759094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:27.226047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:43.920830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:47.989306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:50.986538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:53.971311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:58.127828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:01.006016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:03.695629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:06.553430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:10.577768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:13.770386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:16.794564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:19.932259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:24.107965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:27.420106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:44.311886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:48.190340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:51.192115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:54.179930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:58.331040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:01.194416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:03.900757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:06.746074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:10.903702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:13.978037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:16.997787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:20.139589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:24.412781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:27.638391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:44.681561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:48.395592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:51.406331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:54.375593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:58.531396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:01.410295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:04.118133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:06.948693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:11.126266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:14.197908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:17.222367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:20.377116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:24.626209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:27.830886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:45.026054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:48.584646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:51.603873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:54.661004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:58.721321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:01.625512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:04.334819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:07.170600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:11.321120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:14.389116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:17.438403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:20.589835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:24.836527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:28.024432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:45.372909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:48.775757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:51.793657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:54.978910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:58.909288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:01.834425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:04.529027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:07.351188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:11.519158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:14.587607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:17.644197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:20.794030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:25.033825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:28.222013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:45.706222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:48.986706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:52.015675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:55.311510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:59.112005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:02.022801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:04.709457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:07.564164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:11.751108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:14.791881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:17.853738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:21.059216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:25.258486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:28.421492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:46.044351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:49.190682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:52.221189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:55.626102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:42:59.309360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:02.212294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:04.927022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:07.782605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:11.962918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:14.998954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:18.071691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:21.323090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:25.475838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-07-30T07:43:28.788533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T07:43:29.383424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T07:43:29.794674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

843YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
11970NaN68.43NaNNaNNaNNaN89.20NaN157.63178.81NaNNaNNaN55.73885.499.66NaN85.97181.49NaN1397.131554.76
21971NaN86.77NaNNaNNaNNaN84.09NaN170.85210.70NaNNaNNaN53.15933.358.28NaN88.11469.12NaN1762.721933.58
31972NaN93.30NaNNaNNaNNaN73.66NaN166.95209.50NaNNaNNaN59.161099.036.90NaN90.26431.12NaN1895.972062.92
41973NaN91.43NaNNaNNaNNaN129.65NaN221.08220.24NaNNaNNaN113.171334.67NaNNaN103.16537.92NaN2309.162530.24
51974NaN127.82NaNNaNNaNNaN125.18NaN252.99212.25NaNNaNNaN125.171409.23NaNNaN99.29887.14NaN2733.082986.07
61975NaN139.01NaNNaNNaNNaN138.59NaN277.60224.03NaNNaNNaN139.741520.61NaNNaN122.93874.69NaN2882.003159.60
71976NaN136.21NaNNaNNaNNaN115.60NaN251.81264.52NaNNaNNaN144.031643.69NaNNaN159.06908.76NaN3120.063371.86
81977NaN149.27NaNNaNNaNNaN563.93NaN713.21274.40NaNNaNNaN151.751712.29NaNNaN163.35896.31NaN3198.113911.32
91978NaN145.54NaNNaNNaNNaN1235.80NaN1381.34305.44NaNNaNNaN158.601720.75NaNNaN174.981140.05NaN3499.824881.16
101979NaN150.21NaNNaNNaNNaN2035.19NaN2185.39338.88NaNNaNNaN224.621783.72NaNNaN167.671223.91NaN3738.805924.19
843YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
432012NaN5243.68NaNNaNNaNNaN10517.80NaN15761.492267.260.29NaNNaN1187.17341.21NaNNaN193.263107.15NaN7096.3322857.81
442013NaN5808.27NaNNaNNaNNaNNaNNaN5808.272552.2878.04NaNNaN1304.73345.73NaNNaN187.463599.74NaN8067.9713876.24
452014NaN6290.71NaNNaNNaNNaN12477.99NaN18768.702680.885.26NaNNaN1510.10303.96NaNNaN164.423988.67NaN8653.2827421.98
462015NaN6096.36NaNNaNNaNNaN13167.47NaN19263.832744.3729.32NaNNaN1335.81237.66NaNNaN188.393959.76NaN8495.3127759.14
472016NaN6543.13NaNNaNNaNNaN12248.44NaN18791.572554.050.64NaNNaN972.27202.03NaNNaN206.203570.78NaN7505.9726297.54
482017NaN6526.26NaNNaNNaNNaN11937.01NaN18463.272542.8826.060.0NaN886.91176.92NaNNaN202.283702.00NaN7537.0526000.32
492018NaN7216.49NaNNaNNaNNaN14309.58NaN21526.062699.10NaNNaNNaN839.66168.51NaNNaN208.903167.63NaN7083.8128609.87
502019NaN6580.29NaNNaNNaNNaN15153.56NaN21733.852810.69NaNNaNNaN903.45180.78NaNNaN190.423183.60NaN7268.9529002.79
512020NaN4973.66NaNNaNNaNNaN14051.31NaN19024.963287.51NaNNaNNaN500.33108.65NaNNaN175.043213.01NaN7284.5426309.50
522021NaN4706.08NaNNaNNaNNaN12887.10NaN17593.183333.63NaNNaNNaN509.02131.89NaNNaN205.163078.97NaN7258.6624851.84